import torch
import text_preprocess as tp

batch_size = 32
num_steps = 35
train_iter,vocab = tp.load_data_time_machine(batch_size,num_steps)

def get_params(vocab_size,num_hiddens,device):
    num_inputs = num_outputs = vocab_size
    def normal(shape):
        return torch.randn(size=shape,device=device) * 0.01
    def three():
        return (normal((num_inputs,num_hiddens)),
                normal((num_hiddens,num_hiddens)),
                torch.zeros(num_hiddens,device=device))
    
    W_xi, W_hi, b_i = three()
    W_xf, W_hf, b_f = three()
    W_xo, W_ho, b_o = three()
    W_xc, W_hc, b_c = three()

    W_hq = normal((num_hiddens,num_outputs))
    b_q = torch.zeros(num_outputs,device=device)
    params = [W_xi,W_hi,b_i,W_xf,W_hf,b_f,W_xo,W_ho,b_o,W_xc,W_hc,b_c,W_hq,b_q]
    for p in params:
        p.requires_grad_(True)
    return params

def init_lstm_state(batch_size,num_hiddens,device):
    return (torch.zeros((batch_size,num_hiddens),device=device),
            torch.zeros((batch_size,num_hiddens),device=device))

def lstm(inputs,state,params):
    W_xi,W_hi,b_i,W_xf,W_hf,b_f,W_xo,W_ho,b_o,W_xc,W_hc,b_c,W_hq,b_q = params
    (H,C) = state
    outputs = []
    for X in inputs:
        I = torch.sigmoid((X @ W_xi) + (H @ W_hi) + b_i)
        F = torch.sigmoid((X @ W_xf) + (H @ W_hf) + b_f)
        O = torch.sigmoid((X @ W_xo) + (H @ W_ho) + b_o)
        C_tilda = torch.tanh((X @ W_xc) + (H @ W_hc) + b_c)
        C = F * C + I * C_tilda
        H = O * torch.tanh(C)
        Y = (H @ W_hq) + b_q
        outputs.append(Y)
    return torch.cat(outputs,dim=0), (H,C)

